What do you even charge for that? We might never make a repository of human made content with no Ai postings in it ever again. Seems like selling the golden goose to me
In other words, the root problem is incompetent management, not any technical issue.
Don't worry though, the legal system is still coming for Musk, and he will be forced to cough up the additional billions (?) he has unlawfully cheated out of a wide assortment of counterparties in violation of his various contracts. And as employee attrition continues, whatever technical problems Twitter has today will only get worse, with or without "scraping".
(And IMO, that perceived legitimacy was unfounded for both HTTPS and the blue check before both were easy to get, it's just that the bar had to drop to the floor for most people to realize how little it meant.)
He got laughed out of a Twitter call thing with lead engineers in the industry for saying he wanted to “rewrite the entire stack” and not having a definition for what he meant.
Doomed or not, Musk is terrible at just about everything he does and Twitter is no exception
The key benefit to a cache is that a small set of content accounts for a large set of traffic. This can be staggeringly effective with even a very limited amount of caching.
Your options are:
1. Maintain the same cache size. This means your origin servers get far more requests, and that you perform far more cache evictions. Both run "hotter" and are less efficient.
2. Increase the cache size. Problem here is that you're moving a lot of low-yield data to the cache. On average it's ... only requested once, so you're paying for far more storage, you're not reducing traffic by much (everything still has to be served from origin), and your costs just went up a lot.
3. Throttle traffic. The sensible place to do this IMO would be for traffic from the caching layer to the origin servers, and preferably for requesting clients which are making an abnormally large set of non-cached object requests. Serve the legitimate traffic reasonably quickly, but trickle out cold results to high-demand clients slowly. I don't know to what extent caching systems already incorporate this, though I suspect at least some of this is implemented.
4. Provide an alternate archival interface. This is its own separately maintained and networked store, might have regulated or metered access (perhaps through an API), might also serve out specific content on a schedule (e.g., X blocks or Y timespan of data are available at specific times, perhaps over multipath protocols), to help manage caching. Alternatively, partner with a specific datacentre provider to serve the data within given facilities, reducing backbone-transit costs and limitations.
5. Drop-ship data on request. The "stationwagon full of data tapes" solution.
6. Provide access to representative samples of data. LLM AI apparently likes to eat everything it can get its hands on, but for many purposes, selectively-sampled data may be sufficient for statistical analysis, trendspotting, and even much security analysis. Random sampling is, through another lens, an unbiased method for discarding data to avoid information overload.
https://www.wired.com/story/twitter-data-api-prices-out-near...
Researchers are complaining that it's far too high for academic grants. Probably true, but that's no different from other obscenely priced subscriptions like access to satellite imagery (can easily be $1k for a single image which you have no right to distribute). I'm less convinced that it's impossible for them to do research with 50 million tweets a month, or with what data there is available. Most researchers can't afford any of the AI SAAS company subscriptions anyway. Data labelling platforms - without the workers - can cost 10-20k a year. I spoke to one company that wouldn't get out of bed for a contract less than 100k. Most offer a free tier a la Matlab in the hope that students will spin out companies and then sign up. I don't have an opinion on what archival tweets should cost, but I do think it's an opportunity to explore more efficient analyses.
Wow, never thought of it that way before. Kinda hit me hard for some reason.
I think "AI is takin' ooor contents!" is a convenient excuse to tighten the screws further. Having a Boogeyman in the form of technology that's already under worried discussion by press and politicians is a great way to convince users how super-super-serious the problem must be, and to blow a dog whistle at other companies to indicate they should so the same.
It's no coincidence that the first two companies to do this so actively and recently are both overvalued, not profitable, and don't actually directly produce any of the content on their platforms.